1. Field of the Invention
The present invention generally relates to methods and systems for detection of defects in relatively noisy inspection data.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection of semiconductor wafers is and will continue to be of significant importance in semiconductor manufacturing. In addition, the ability of inspection tools or systems to detect a range of defect types may determine how well semiconductor fabrication processes can be monitored and controlled. Consequently, there have been significant efforts in improving the processing of inspection data to increase the accuracy with which defects can be detected.
Most inspection data processing involves two steps: defect detection and then classification. For instance, on many commercially available inspection systems, defects are found by detecting point defects via signal thresholding on individual data points in simple one-dimensional scans. Individual point defects may then be displayed on a point defect map or organized into another format. The point defect map is then post-processed to recognize if several of the points fall roughly into a two-dimensional shape, at which point that collection of points is labeled or classified as a specific defect instead of as individual particle defects.
There are, however, several disadvantages to the above methods of inspection data processing for detecting the presence of particular types of defects. In particular, these methods can be relatively inaccurate when detecting defects in relatively noisy inspection data. For example, as described above, simple one-dimensional scans only generate raw signals at individual points on the substrate, and every encounter with a two-dimensional surface anomaly is treated as a disconnected collection of point defects. Therefore, signal thresholding yields a defect map determined solely by signal strength at the individual points. Consequently, portions of faint two-dimensional defects may be lost to background noise due to failure of some of its associated point defect signals to exceed the threshold. As a result, the above-described methods for detecting defects may be substantially inaccurate when detecting defects in noisy inspection data since many defects may not be detected at all. In addition, the above-described methods for detecting defects may be substantially inaccurate in detecting the types of defects that are present on a substrate since portions of defects may not be detected thereby increasing the probability of misclassification of defects.
Accordingly, it may be advantageous to develop methods and systems for detecting defects on a substrate that are substantially accurate for detecting a range of defect types, particularly in relatively noisy inspection data.